Name
Abstract | ||
|---|---|---|
The derivation of quantitative information from images to make quantitative radiology (QR) clinically practical continues to face a major image analysis hurdle because of image segmentation challenges. This paper presents a novel approach to disease quantification (DQ) via positron emission tomography/computed tomography (PET/CT) images that explores how to decouple DQ methods from explicit dependence on object segmentation through the use of only object recognition results to quantify disease burden. The concept of an object-dependent disease map is introduced to express disease severity without performing explicit delineation and partial volume correction of either objects or lesions. The parameters of the disease map are estimated from a set of training image data sets. The idea is illustrated on 20 lung lesions and 20 liver lesions derived from F-18-2-fluoro-2-deoxy-D-glucose (FDG)-PET/CT scans of patients with various types of cancers and also on 20 NEMA PET/CT phantom data sets. Our preliminary results show that, on phantom data sets, "disease burden" can be estimated to within 2% of known absolute true activity. Notwithstanding the difficulty in establishing true quantification on patient PET images, our results achieve 8% deviation from "true" estimates, with slightly larger deviations for small and diffuse lesions where establishing ground truth becomes really questionable, and smaller deviations for larger lesions where ground truth set up becomes more reliable. We are currently exploring extensions of the approach to include fully automated body-wide DQ, extensions to just CT or magnetic resonance imaging (MRI) alone, to PET/CT performed with radiotracers other than FDG, and other functional forms of disease maps. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/12.2254907 | Proceedings of SPIE |
Keywords | Field | DocType |
Disease quantification,cancer,object recognition,PET/CT | Computer vision,PET-CT,Data set,Segmentation,Imaging phantom,Image segmentation,Ground truth,Artificial intelligence,Positron emission tomography,Magnetic resonance imaging,Physics | Conference |
Volume | ISSN | Citations |
10137 | 0277-786X | 1 |
PageRank | References | Authors |
0.35 | 3 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yubing Tong | 1 | 93 | 22.73 |
| Jayaram K. Udupa | 2 | 2481 | 322.29 |
| Dewey Odhner | 3 | 339 | 43.49 |
| Caiyun Wu | 4 | 16 | 8.46 |
| Danielle M. Fitzpatrick | 5 | 1 | 0.35 |
| Nicole Winchell | 6 | 1 | 0.35 |
| Stephen J. Schuster | 7 | 1 | 0.35 |
| D. A. Torigian | 8 | 81 | 21.68 |